Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation

Emily Öhman, Kaisla Kajava, Jörg Tiedemann, Timo Honkela

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

This paper introduces a gamified framework for fine-grained sentiment analysis and emotion detection. We present a flexible tool, Sentimentator, that can be used for efficient annotation based on crowd sourcing and a self-perpetuating gold standard. We also present a novel dataset with multi-dimensional annotations of emotions and sentiments in movie subtitles that enables research on sentiment preservation across languages and the creation of robust multilingual emotion detection tools. The tools and datasets are public and open-source and can easily be extended and applied for various purposes.

Original languageEnglish
Title of host publicationWASSA 2018 - 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages24-30
Number of pages7
ISBN (Electronic)9781948087803
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2018 - Brussels, Belgium
Duration: 2018 Oct 31 → …

Publication series

NameWASSA 2018 - 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Proceedings of the Workshop

Conference

Conference9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2018
Country/TerritoryBelgium
CityBrussels
Period18/10/31 → …

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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